Excessive inflammatory and metabolic responses to acute SARS-CoV-2 infection are associated with a distinct gut microbiota composition

  1. Werner C. Albrich
  2. Tarini Shankar Ghosh
  3. Sinead Ahearn-Ford
  4. Flora Mikaeloff
  5. Nonhlanhla Lunjani
  6. Brian Forde
  7. Noémie Suh
  8. Gian-Reto Kleger
  9. Urs Pietsch
  10. Manuel Frischknecht
  11. Christian Garzoni
  12. Rossella Forlenza
  13. Mary Horgan
  14. Corinna Sadlier
  15. Tommaso Rochat Negro
  16. Jérôme Pugin
  17. Hannah Wozniak
  18. Andreas Cerny
  19. Ujjwal Neogi
  20. Paul W. O’Toole
  21. Liam O’Mahony

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Abstract

Protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and associated clinical sequelae requires well-coordinated metabolic and immune responses that limit viral spread and promote recovery of damaged systems. In order to understand potential mechanisms and interactions that influence coronavirus disease 2019 (COVID-19) outcomes, we performed a multi-omics analysis on hospitalised COVID-19 patients and compared those with the most severe outcome (i.e. death) to those with severe non-fatal disease, or mild/moderate disease, that recovered. A distinct subset of 8 cytokines and 140 metabolites in sera identified those with a fatal outcome to infection. In addition, elevated levels of multiple pathobionts and lower levels of protective or anti-inflammatory microbes were observed in the faecal microbiome of those with the poorest clinical outcomes. Weighted gene correlation network analysis (WGCNA) identified modules that associated severity-associated cytokines with tryptophan metabolism, coagulation-linked fibrinopeptides, and bile acids with multiple pathobionts. In contrast, less severe clinical outcomes associated with clusters of anti-inflammatory microbes such as Bifidobacterium or Ruminococcus , short chain fatty acids (SCFAs) and IL-17A. Our study uncovered distinct mechanistic modules that link host and microbiome processes with fatal outcomes to SARS-CoV-2 infection. These features may be useful to identify at risk individuals, but also highlight a role for the microbiome in modifying hyperinflammatory responses to SARS-CoV-2 and other infectious agents.

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  1. ScreenIT

    SciScore for 10.1101/2021.10.26.465865: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: All patients or patient representatives signed a patient informed consent.
    IRB: The study was approved by local ethics committees (EKOS 20/058 for the three Swiss sites and The Clinical Research Ethics Committee of the Cork Teaching Hospitals for Cork University Hospital).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    The mediators measured included IL-1α, IL-1β, IL-1RA, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12/23p40, IL-12p70, IL-13, IL-15, IL-16, IL-17A, IL-17A/F, IL-17B, IL-17C, IL-21, IL-22, IL-23, IL-27, IL-31, TNF-α, TNF-β, IFN-γ, IP-10, MIP-1α, MIP-1β, MIP-3α, MCP-1, MCP-4, Eotaxin, Eotaxin-3, TARC, MDC, TSLP, CRP, SAA, VEGF-A, VEGF-C, VEGF-D, sTie-2, Flt-1, sICAM-1, sVCAM-1, bFGF, PIGF and GM-CSF. Metabolomics: Untargeted metabolomics on patient sera was performed by MetabolonTM using the HD4 platform.
    MCP-1
    suggested: None
    Software and Algorithms
    SentencesResources
    Pairwise differential abundance analysis was performed between conditions using R package LIMMA.
    LIMMA
    suggested: (LIMMA, RRID:SCR_010943)
    Python 3 gseapy package was used to perform a hypergeometric test between list of significant metabolites and reference.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Importance plots, dot plots, bar plots, pca plots were produced with R package ggplot2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Heatmaps were designed with the R package ComplexeHeatmap.
    ComplexeHeatmap
    suggested: None
    Networks were represented using Cytoscape 3.6.1 and metabolites of interest highlighted.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    For the microbiome analysis, the raw Illumina reads obtained for each sample were quality-filtered using the trimmomatic program, using the default parameters47.
    trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Estimates of alpha diversity were computed using the diversity function of the vegan package of R.
    vegan
    suggested: (vegan, RRID:SCR_011950)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2021.10.26.465865 on bioRxiv